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Definition
The Post-RAndomization Method (PRAM) is a probabilistic, perturbative masking method for disclosure protection of categorical microdata. In the masked file, the scores on some categorical attributes for certain records in the original file are changed to a different score according to a prescribed probability mechanism, namely a Markov matrix. The Markov approach makes PRAM very general, because it encompasses noise addition, data suppression and data recoding.
Key Points
The PRAM matrix contains a row for each possible value of each attribute to be protected. This rules out using the method for continuous data. PRAM was invented by Gouweleeuw et al. [1]. The information loss and disclosure risk in data masked with PRAM largely depend on the choice of the Markov matrix and are still (open) research topics [2].
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Recommended Reading
Gouweleeuw J.M., Kooiman P., Willenborg L.C.R.J., and DeWolf P.-P. 1997. Post randomisation for statistical disclosure control: Theory and implementation, 1997. Statistics Netherlands).(Voorburg: Research Paper No. 9731
de Wolf P.-P. Risk, utility and PRAM. In J. Domingo-Ferrer and L. Franconi (eds.). Privacy in Statistical Databases LNCS, vol. 4302, 2006, pp. 189–204.
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© 2009 Springer Science+Business Media, LLC
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Domingo-Ferrer, J. (2009). PRAM. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_1499
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DOI: https://doi.org/10.1007/978-0-387-39940-9_1499
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